ALPR is an image-processing approach that often functions as the core module of “intelligent” transportation infrastructure applications. License plate recognition techniques, such as ALPR, can be employed to identify a vehicle by automatically reading a license plate utilizing image processing and character recognition technologies. A license plate recognition operation can be performed by locating a license plate in an image, segmenting the characters in the captured image of the plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified.
The ALPR problem is often decomposed into a sequence of image processing operations: locating the sub-image containing the license plate (i.e., plate localization), extracting images of individual characters (i.e., segmentation), and performing optical character recognition (OCR) on these character images. In order for OCR to achieve high accuracy, it is necessary to obtain properly segmented characters.
There are a number of challenging noise sources present in license plate images captured under realistic conditions (i.e., field deployed solutions). These include: heavy shadows, non-uniform illumination (from one vehicle to the next, daytime versus nighttime, etc.), challenging optical geometries (tilt, shear, or projective distortions), plate frames and/or stickers partially touching characters, partial occlusion of characters (e.g., trailer hitch ball), poor contrast, and general image noise (e.g., salt and pepper noise). For some ALPR systems deployed in the United States, variation between states in character font, width, and spacing further add to the difficulty of proper character segmentation.
Current character segmentation subsystems within ALPR applications are structured in two stages. The first stage calculates a vertical projection histogram (a very common segmentation technique) to produce initial character boundaries (cuts), and uses local statistical information, such as median character spacing, to split large cuts (caused by combining characters) and insert missing characters. The operations applied in the first stage require minimal computational resources and consequently are applied to each input image to achieve good character segmentation accuracy. No a-priori image information is utilized in this first stage, enabling robust performance over a variety of state logos, fonts, and character spacing.
The second stage classifies the segmented images as likely to be a valid image for downstream analysis or suspect as invalid and further improves segmentation performance by applying additional analysis to the suspect character images. This additional analysis includes: 1) application of OCR followed by application of state-specific rules to determine validity of suspect characters, and 2) combining of adjoining suspect narrow characters and assessment of OCR confidence of the combined character.
A problem arises when the first stage fails to produce a result that is reasonably close to the correct answer. This is manifested by two or more valid character images missing from the output of the first stage due to an insufficient number of segmentation cuts. The second stage can recover one and in limited cases two character errors, but in general if two or more characters are missing the results will be missing a valid character image coming out of segmentation and regardless of how good OCR or State ID perform, there is no opportunity to obtain the correct license plate.